Nonhydrostatic Multiscale Model Research Articles

Rapid Update with EnVar Direct Radar Reflectivity Data Assimilation for the NOAA Regional Convection-Allowing NMMB Model over the CONUS: System Description and Initial Experiment Results

This study first describes the extended Grid-Point Statistical Interpolation analysis system (GSI)-based ensemble-variational data assimilation (DA) system within the North American Mesoscale Rapid Refresh (NAMRR) system for the Nonhydrostatic Multiscale Model on the B grid (NMMB). Experiments were conducted to examine three critical aspects of data assimilation configuration in this system. Ten retrospective high-impact convective cases during the warm season of 2015–2016 were adopted for testing. A 10-member, 18 h ensemble forecast was launched for each experiment. Specifically, the experiment using horizontal (vertical) localization radii (Lr) of 300 km (0.55-scaled height measured in the nature log of pressure) overall had more skills than that of 500 km (1.1-scaled height) for conventional in-situ observation assimilation. Diagnostics suggest that the higher forecast skills could be attributed to applying smaller Lr in the boundary with large temperature and moisture gradients. For radar DA, the experiment was more skillful with horizontal (vertical) Lr of 15 km (1.1-scaled height) than that of 12 km (0.55-scaled height). Diagnostics suggest that the improved forecasts were achieved by using wider Lr to spread radar observations into unobserved areas more effectively. Slight forecast skill differences between the relaxation inflation factors of 95% and 65% are presented. The impact of varying inflation magnitudes primarily occurred in the upper-level spread.

The influence of future climate change on wind energy potential in the Republic of Serbia

In this paper, we analyzed the influence of future climate change on wind energy potential in the Republic of Serbia. We used climate projections from two regional climate models: the coupled regional climate model Eta Belgrade University-Princeton Ocean Model (EBU-POM) and the Nonhydrostatic Multiscale Model on the B grid (NMMB). First, the daily wind speed at 10 m above ground that was simulated with both models was verified against observed data at 13 meteorological stations in the reference period, 1971–2000. Then, the wind speed at 10 m above ground was bias corrected and extrapolated to 70 m above ground, and the wind energy density was calculated at the same height. Next, the wind speed and wind density energy in a future period, 2071–2100, were calculated and compared with the reference period. Based on the analyzed results, the annual wind speed from the EBU-POM model underestimates the observed values at all stations, while the annual wind speed from NMMB model overestimates the observed values at all stations. In comparison with the reference period, the annual wind speed in the future will decrease in Serbia. These changes are small but statistically significant. On the other hand, analyses for different seasons revealed that seasonal changes in wind speed will be in the range of ± 5% relative to the reference period.

Comparing the Assimilation of Radar Reflectivity Using the Direct GSI-Based Ensemble–Variational (EnVar) and Indirect Cloud Analysis Methods in Convection-Allowing Forecasts over the Continental United States

Abstract Two methods for assimilating radar reflectivity into deterministic convection-allowing forecasts were compared: an operationally used, computationally less expensive cloud analysis (CA) scheme and a relatively more expensive, but rigorous, ensemble Kalman filter–variational hybrid method (EnVar). These methods were implemented in the Nonhydrostatic Multiscale Model on the B-grid and were tested on 10 cases featuring high-impact deep convective storms and heavy precipitation. A variety of traditional, neighborhood-based, and features-based verification metrics support that the EnVar produced superior free forecasts compared to the CA procedure, with statistically significant differences extending up to 9 h into the forecast. Despite being inferior, the CA scheme was able to provide benefit compared to not assimilating radar reflectivity at all, but limited to the first few forecast hours. While the EnVar is able to partially suppress spurious convection by assimilating 0-dBZ reflectivity observations directly, the CA is not designed to reduce or remove hydrometeors. As a result, the CA struggles more with suppression of spurious convection in the first-guess field, which resulted in high-frequency biases and poor forecast evolution, as illustrated in a few case studies. Additionally, while the EnVar uses flow-dependent ensemble covariances to update hydrometers, thermodynamic, and dynamic variables simultaneously when the reflectivity is assimilated, the CA relies on a radar reflectivity-derived latent heating rate that is applied during a separate digital filter initialization (DFI) procedure to introduce deep convective storms into the model, and the results of CA are shown to be sensitive to the window length used in the DFI.

Regional modeling of dust storm of February 8, 2015 in the southwest of Iran

According to World Meteorological Organization report in 2015, the southwest of Iran has become one of the dust sources in the region. And the objective of this research is to study the dust storms originating in this region. For this purpose, based on the weather data of 14 stations, the dust storms of the region were investigated, and the dust storm of February 7, 2015, was selected due to its very high concentration of dust particles (66 times the normal values). For the analysis of source areas and storm paths, the FNL data was used. The regional models of Navy Aerosol Analysis and Prediction System (NAAPS), Barcelona Supercomputing Centre-Dust Regional Atmospheric Model 8b (DREAM 8b), Non-hydrostatic Multiscale Model Barcelona Supercomputing Center (NMMB/BSC), and hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) were used to study and analyze the selected storm. The results showed that the dust event in February 8, 2016, has been the result of the polar front jet stream (PJF) caused by western immigrant system that had been over the Sahara in Africa, the deserts of Iraq, Syria, Saudi Arabia, and finally southwest of Iran in making the extreme dust event. According to the Moderate Resolution Imaging Spectroradiometer data and point models of NAAPS, optical depth was very high. The DREAM 8b and NMMB/BSC models confirmed the impact of the local factors and closeness to the dust source regions. The backward tracking of the model with the HYSPLIT model showed three tracks transporting the dust particles to the region. This software also showed that the dust particles occupied an atmospheric tunnel of 1.5 km in diameter.

A Nonhydrostatic Multiscale Model on the Uniform Jacobian Cubed Sphere

The rapid expansion of contemporary computers is expected to enable operational integrations of global models of the atmosphere at resolutions close to 1 km, using tens of thousands of processors in the foreseeable future. Consequently, the algorithmic approach to global modeling of the atmosphere will need to change in order to better adjust to the new computing environment. One simple and convenient solution is to use low-order finite-differencing models, which generally require only local exchange of messages between processing elements, and thus are more compatible with the new computing environment. These models have already been tested with physics and are well established at high resolutions over regional domains. A global nonhydrostatic model, the Nonhydrostatic Multiscale Model on the B grid (NMMB), developed at the Environmental Modeling Center of the National Centers for Environmental Prediction during the first decade of this century is one such model. A drawback of the original version of global NMMB is that it is discretized on the standard longitude–latitude grid and requires application of Fourier polar filtering, which is relatively inefficient on massively parallel computers. This paper describes a reformulation of the NMMB on the grid geometry of a novel cubed sphere featuring a uniform Jacobian of the horizontal mapping, which provides a uniform resolution close to that of the equiangular gnomonic cubed sphere, but with a smooth transition of coordinates across the edges. The modeling approach and encountered challenges are discussed and several results are shown that demonstrate the viability of the approach.

Optimizing Weather Model Radiative Transfer Physics for Intel’s Many Integrated Core (MIC) Architecture

Large numerical weather prediction (NWP) codes such as the Weather Research and Forecast (WRF) model and the NOAA Nonhydrostatic Multiscale Model (NMM-B) port easily to Intel's Many Integrated Core (MIC) architecture. But for NWP to significantly realize MIC’s one- to two-TFLOP/s peak computational power, we must expose and exploit thread and fine-grained (vector) parallelism while overcoming memory system bottlenecks that starve oating-point performance. We report on our work to improve the Rapid Radiative Transfer Model (RRTMG), responsible for 10-20 percent of total NMM-B run time. We isolated a standalone RRTMG benchmark code and workload from NMM-B and then analyzed performance using hardware performance counters and scaling studies. We restructured the code to improve vectorization, thread parallelism, locality, and thread contention. The restructured code ran three times faster than the original on MIC and, also importantly, 1.3x faster than the original on the host Xeon Sandybridge.

Mesoscale Model Evaluation Testbed (MMET): A Resource for Transitioning NWP Innovations from Research to Operations (R2O)

Abstract A wide range of numerical weather prediction (NWP) innovations are under development in the research community that have the potential to positively impact operational models. The Developmental Testbed Center (DTC) helps facilitate the transition of these innovations from research to operations (R2O). With the large number of innovations available in the research community, it is critical to clearly define a testing protocol to streamline the R2O process. The DTC has defined such a process that relies on shared responsibilities of the researchers, the DTC, and operational centers to test promising new NWP advancements. As part of the first stage of this process, the DTC instituted the mesoscale model evaluation testbed (MMET), which established a common testing framework to assist the research community in demonstrating the merits of developments. The ability to compare performance across innovations for critical cases provides a mechanism for selecting the most promising capabilities for further testing. If the researcher demonstrates improved results using MMET, then the innovation may be considered for the second stage of comprehensive testing and evaluation (T&E) prior to entering the final stage of preimplementation T&E. MMET provides initialization and observation datasets for several case studies and multiday periods. In addition, the DTC provides baseline results for select operational configurations that use the Advanced Research version of Weather Research and Forecasting Model (ARW) or the National Oceanic and Atmospheric Administration (NOAA) Environmental Modeling System Nonhydrostatic Multiscale Model on the B grid (NEMS-NMMB). These baselines can be used for testing sensitivities to different model versions or configurations in order to improve forecast performance.

Influence of dust accumulation on building roof thermal performance and radiant heat gain in hot-dry climates

This paper presents an effort to estimate the impact of dust accumulation on exterior building roof absorptivity and total radiative heat gain. A new model is introduced to calculate a building solar absorptivity as a function of dust accumulation rate. Hourly dust deposition is modeled using the Non-hydrostatic Multi-scale Model (NMMB) to predict monthly averaged dust accumulation over time. The correlation sensitivities to its input parameters and the impact of dust accumulation on building annual loads are also studied. Results show that dust accumulation increases the roof solar absorptivity from its initial value up to dust absorptivity based on the site climatic condition and roof characteristics. The predicted monthly averaged accumulated dust for all studied sites varies between 1.3 and 73.8g/m2/month. The new model has resulted in an annual cooling space increase of 44.7–181.1kWh/m2/year, for the selected hot-dry sites with moderate to extreme dust storm conditions. Heating reductions were found to be 0.5–13.1kWh/m2/year which are not significant in comparison to the increase in annual cooling load. The results of this work were attempted to improve the predictive capability of current building simulation models.

Verification of the New Nonhydrostatic Multiscale Model on the B Grid (NMMB): A View on Global Predictability of Surface Parameters

Abstract A global verification of temperature, dewpoint temperature, and wind speed for the new Nonhydrostatic Multiscale Model on the B Grid (NMMB) is computed for a 3-yr period (2010–12) using over 9000 weather stations. The raw model forecasts, as well as bias-removed MOS forecasts, are analyzed and compared to NOAA’s operational GFS. In comparison to the GFS, the NMMB forecasts of temperature, dewpoint temperature, and wind speed are about 10% better, even though the NMMB is run at much coarser resolution and does not yet have its own data assimilation system. However, as a result of several changes in the GFS during the 3-yr period, the MOS computations for GFS are not optimal. Using unbiased MOS forecasts, the global distribution of spatial predictability can be analyzed. Clear spatial patterns emerge, which are partly dependent on the variable. For temperature, the best forecasts can be made for small islands and coastlines, and a clear gradient of decreasing skill with increasing distance from the sea is visible on the continents. For wind speed, this pattern is almost reversed. Dewpoint temperature shows the largest patterns, mainly controlled by the humidity of the climate. Combining temperature, wind speed, and dewpoint temperature in a gross predictability index reveals a clear large-scale pattern. Remarkably, smaller-scale features like mountain ranges are not readily apparent in the bias-free predictability pattern, indicating that the spatial pattern of the gross predictability is controlled at the very large scales.

Atmospheric dust modeling from meso to global scales with the online NMMB/BSC-Dust model – Part 1: Model description, annual simulations and evaluation

Abstract. We describe and evaluate the NMMB/BSC-Dust, a new dust aerosol cycle model embedded online within the NCEP Non-hydrostatic Multiscale Model (NMMB). NMMB is a further evolution of the operational Non-hydrostatic Mesoscale Model (WRF-NMM), which together with other upgrades has been extended from meso to global scales. Its unified non-hydrostatic dynamical core is prepared for regional and global simulation domains. The new NMMB/BSC-Dust is intended to provide short to medium-range weather and dust forecasts from regional to global scales and represents a first step towards the development of a unified chemical-weather model. This paper describes the parameterizations used in the model to simulate the dust cycle including sources, transport, deposition and interaction with radiation. We evaluate monthly and annual means of the global configuration of the model against the AEROCOM dust benchmark dataset for year 2000 including surface concentration, deposition and aerosol optical depth (AOD), and we evaluate the daily AOD variability in a regional domain at high resolution covering Northern Africa, Middle East and Europe against AERONET AOD for year 2006. The NMMB/BSC-Dust provides a good description of the horizontal distribution and temporal variability of the dust. Daily AOD correlations at the regional scale are around 0.6–0.7 on average without dust data assimilation. At the global scale the model lies within the top range of AEROCOM dust models in terms of performance statistics for surface concentration, deposition and AOD. This paper discusses the current strengths and limitations of the modeling system and points towards future improvements.